1) Use dot notation to access attributes

The Fn::GetAtt intrinsic function supports the use of dot notation to refer to a resource’s attribute. It works both the long Fn::GetAtt and short !GetAtt forms of the syntax. Instead of the long, enumerated, YAML array syntax:

Value:
Fn::GetAtt:
- MyResource
- Arn

Use the one-liner version:

Value: !GetAtt MyResource.Arn

2) Use !Sub instead of !Join

When creating a string, using !Sub is often a much better option than using !Join. Here’s an example of building an ECR repository URI:

A cool thing to notice is the explicit !Ref on pseudo-parameters and template parameters. This makes the whole construction so much nicer. If you need to access an attribute, you can use the dot notation as mentioned above.

To my surprise, I found out I did not have to do anything at all. DynamoDB keeps the connection open. See for yourself – using the CLI, run aws dynamodb list-tables --debug. Notice the response headers section, which looks something like this:

The Connection: keep-aliveheader isset by DynamoDB. Unless it’s explicitly set to close, the connection will stay open. Yet this is exactly what NodeJS does. Thank you to Stefano Buliani for providing additional visibility into this. This behaviour is inherited by the aws-js-sdk. I think that’s a mistake so I’ve opened a bug in the GitHub repo. Until then, if you’re writing code in JS, be sure to follow Yan’s recommendation.

Connection: keep-alive vs. close in Python

I was still curious if I could replicate Yan’s findings in Python. Here’s a log of running a single putItem operation using vanilla boto3 DynamoDB client:

Except for the first one, most of them are sub 10 ms, since the connection is kept open.

However, when I explicitly did add the Connection: close header, things looked a lot different:

Operations took at least 50 ms, often longer. This is in line with Yan’s findings.

Granted, my approach was not very rigorous. For the sake of replicability, here’s the code I used. Feel free to run your own experiments and let me know what you found.

One of the drawbacks of using a Lambda Layer is that you must declare it by its full version. This is a hassle as every time you update a Layer, you need to update its declaration in every stack to get the latest updates. It would be much better if one could specify it only by its name (similar as with the FunctionName when declaring event source mapping). That is, instead of arn:aws:lambda:us-east-1:123456789012:layer:my-layer:24 just use my-layer.

I made a Cloudformation macro to do just that.

Uselatest scans through a Cloudformation template and replaces occurrences of Lambda Layers that are not fully qualified with an ARN of the latest available version of that Layer. This way you don’t have to think about updating a template after updating a Layer. The latest version will automatically get picked up during stack deployment. Magic. ✨

The macro works in all the places where you can declare a Layer. Check the Example section for more.

I wanted to make it available in the Serverless App Repo, but sadly, a Cloudformation Macro is not a supported resource. You’ll have to build, package and deploy it yourself if you want to use it.

First, I’m using the wonderful scope functionality of pytest fixtures. This allows me to create a new fixture per every test function execution. It is necessary for Stubber to work correctly.

The Stubber needs to be created with the correct client. Since I’m using a DynamoDB Table instance in models.py, I have to access its client when creating the Stubber instance.

Notice also the “verbose” get_item_response structure in the first test. That’s because of how the DynamoDB client interacts with DynamoDB API (needless to say, this is DynamoDB specific). The Table is a layer of abstraction on top of this, it converts between DynamoDB types and Python types. However it still uses the client underneath, so it expects this structure nevertheless.

Finally, it’s good practice to call assert_no_pending_response to make sure the tested code actually did make the call to an AWS service.

I really like this combination of pytest and Stubber. It’s a great match for writing correct and compact tests.

Following my previous post on judging the serverlessness of a technology, I apply this criterion to AWS Lambda. I argue that the timeout and memory size configuration parameters are non-essential and should be made optional. The need to think about them makes Lambda less serverless than it could be.

On timeout

The way you naturally write a function is to finish as soon as possible. It’s just good engineering and good for business. Why then artificially limit its execution time?

The most common case I hear about using timeout is when a Lambda calls some external API. In this scenario, it is used as a fail-safe in case the API takes too long to respond. A better approach is to implement a timeout on the API call itself, in code, and fail the Lambda gracefully if it does not respond in time instead of relying on the runtime to terminate your function. That’s also good engineering.

So here’s my first #awswishlist entry: Make timeout optional and let functions run as long as they need to.

On memory size

I have two issues with the memory size parameter.

First of all, it’s a leaky abstraction of the underlying system. You don’t just specify how much memory your function gets, but also the CPU power. There’s a threshold where the Lambda container is assigned 2 vCPUs instead of 1. Last time I checked this was at 1024 MB, but there’s no way of knowing this unless you experiment with the platform. Since Lambda does not offer specialized CPU instances like EC2 does (yet?), it might not matter, but I worked on a data processing application where this came into play. Why not allow us to configure this directly? What if I need less memory but more vCPUs?

However a more serious point of contention for me is that setting the memory size is an issue of capacity planning. That’s something that should have gone away in the serverless world. You have to set it for the worst possible scenario as there’s no “auto-scaling”. It really sucks when your application starts failing because a Lambda function suddenly needs 135 MB of memory to finish.

Hence here’s my second #awswishlist entry: Make memory size optional. Or provide “burst capacity” for those times a Lambda crosses the threshold.

Now I won’t pretend I understand all the complexities that are behind operating the Lambda platform and I imagine this is an impossible request, but one can dream.

And while I’m at it, a third #awswishlist item is: Publish memory consumed by a Lambda function as a metric to CloudWatch.

Closing remarks

I do see value in setting either of these parameters, but I think those are specialized cases. For the vast majority of code deployed on Lambda, the platform should take care of “just doing the right thing” and allow us, developers, to think less about the ops side.

Even though serverless has been around for a couple of years now, there is not a clear definition what the term actually means. Leaving aside that it’s a misnomer to begin with, I think part of the confusion stems from the fact that it is being applied to in two different ways. Serverless can either describe a quality of a technology (DynamoDB) or it can refer to an approach of building IT systems (a serverless chat-bot).

My way to judging the former is this:

The less you have to think about servers the more serverless a technology is. Furthermore, serverless is not a binary value but a spectrum.

Let me give an example. On a completely arbitrary scale from 1 to 10, I would rate DynamoDB with provisioned capacity as 8/10 serverless. It’s not fully serverless because I still need to think deeply about data access patterns, predict read and write load and monitor utilization once my system is operational. However, with the recent announcement of on demand pricing, I would rate DynamoDB 10/10. I don’t need to think about any of these aforementioned idiosyncrasies (burdens, really) of using the technology.

The second aspect of a serverless technology (and by proxy also a system) is that you don’t pay for idle except for data storage. Once again, if you need to think about something even if it’s not running (and clearly you’re going to think about your credit card bill), it is not serverless.

This is the promise of serverless. Once you start combining these technologies into systems, you can think about and focus on buildingvalue and leave the operationalcost on the technology provider.